We compare our approach with a wide range of state-of-the-art radiology report generation models, i.e., HRNN [1]}, CoAtt [2]}, HGRG-Agent [3]}, CMAS-RL [4]}, SentSAT+KG [5]}, Transformer [6]} and R2Gen [6]}, as well as four image captioning models, namely CNN-RNN [8]}, AdaAtt [9]}, Att2in [10]} and Up-Down [11]}. For the IU-Xray dataset, except that the results of HRNN are implemented by ourselves, for systems designed for radiology report generation, we report the results from the original papers; For the MIMIC-CXR dataset, we directly cite the results from [6]}. As shown in Table REF , our PPKED outperforms state-of-the-art methods across all metrics on both MIMIC-CXR and IU-Xray datasets. The improved performance of PPKED demonstrate the validity of our practice in exploring and distilling posterior and prior knowledge for radiology report generation.
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